batman

car_price_guesser/predict.py

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+import pandas as pd
+import joblib
+import sys
+import random
+
+# Define features used in training (must match train_model.py)
+FEATURES = [
+    "Year",
+    "Mileage",
+    "brand",
+    "new&used",
+    "Exterior color",
+    "Interior color",
+    "Drivetrain",
+    "MPG",
+    "Fuel type",
+    "Transmission",
+    "Engine",
+    "Convenience",
+    "Entertainment",
+    "Exterior",
+    "Safety",
+    "Seating",
+    "Accidents or damage",
+    "Clean title",
+    "1-owner vehicle",
+    "Personal use only",
+    "Model",
+]
+
+
+def predict_random_row():
+    try:
+        model = joblib.load("car_price_model.joblib")
+    except FileNotFoundError:
+        print(
+            "Error: Model file 'car_price_model.joblib' not found. Please run train_model.py first."
+        )
+        return
+
+    print("Loading data to pick a random row...")
+    try:
+        df = pd.read_csv("New_York_cars.csv")
+    except FileNotFoundError:
+        print("Error: 'New_York_cars.csv' not found.")
+        return
+
+    # Pick a random row
+    random_index = random.randint(0, len(df) - 1)
+    row = df.iloc[[random_index]]
+
+    print(f"\nSelected Row Index: {random_index}")
+    print("-" * 30)
+
+    # Display selected features
+    for feature in FEATURES:
+        val = row[feature].values[0]
+        print(f"{feature}: {val}")
+
+    actual_price = row["money"].values[0]
+    print("-" * 30)
+    print(f"Actual Price: ${actual_price:,.2f}")
+
+    # Predict
+    try:
+        # Ensure we only pass the features the model expects
+        input_data = row[FEATURES]
+        prediction = model.predict(input_data)[0]
+        print(f"Predicted Price: ${prediction:,.2f}")
+
+        diff = prediction - actual_price
+        percent_diff = (diff / actual_price) * 100
+        print(f"Difference: ${diff:,.2f} ({percent_diff:+.2f}%)")
+
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+
+
+def predict_price(year, mileage, brand):
+    # NOTE: This manual function is now limited compared to the full model.
+    # The model now expects many more features.
+    # For now, we will warn the user or try to fill others with defaults/unknowns if possible,
+    # but realistically, manual entry of 20+ features is hard.
+    # We will just try to predict with what we have and let the model pipeline handle missing cols if it can,
+    # or error out.
+
+    print(
+        "Warning: The model now uses many features. Manual entry only supports Year, Mileage, Brand."
+    )
+    print(
+        "Other features will be set to default/unknown values, which may affect accuracy."
+    )
+
+    try:
+        model = joblib.load("car_price_model.joblib")
+    except FileNotFoundError:
+        print("Error: Model file 'car_price_model.joblib' not found.")
+        return
+
+    # Create dataframe with all expected features initialized to NaN or appropriate defaults
+    input_data = pd.DataFrame(columns=FEATURES)
+    input_data.loc[0] = [None] * len(FEATURES)  # Initialize with None
+
+    input_data["Year"] = year
+    input_data["Mileage"] = mileage
+    input_data["brand"] = brand
+
+    # Fill others if necessary (the pipeline handles NaNs for some, but let's see)
+    # The training pipeline uses SimpleImputer, so NaNs should be handled.
+
+    # Predict
+    try:
+        prediction = model.predict(input_data)[0]
+        print(f"\nEstimated Price for {year} {brand} with {mileage} miles:")
+        print(f"${prediction:,.2f}")
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+
+
+def main():
+    print("--- Car Price Guesser ---")
+
+    if len(sys.argv) > 1 and sys.argv[1] == "--random":
+        predict_random_row()
+        return
+
+    if len(sys.argv) == 4:
+        year = int(sys.argv[1])
+        mileage = float(sys.argv[2])
+        brand = sys.argv[3]
+        predict_price(year, mileage, brand)
+    else:
+        print("Options:")
+        print("1. Random Row Verification: uv run predict.py --random")
+        print("2. Manual Entry: uv run predict.py <Year> <Mileage> <Brand>")
+        print("\nEntering interactive manual mode...")
+        try:
+            year = int(input("Year (e.g., 2020): "))
+            mileage = float(input("Mileage (e.g., 50000): "))
+            brand = input("Brand (e.g., Toyota): ")
+            predict_price(year, mileage, brand)
+        except ValueError:
+            print("Invalid input. Please enter numbers for Year and Mileage.")
+
+
+if __name__ == "__main__":
+    main()